Why R docs should show the shape of data early

March 12, 2026 · 6 min read ·Updated May 29, 2026

rdataexamplesdocumentation

A lot of confusion in R docs is not really about syntax. It is about data shape. Readers struggle when they cannot tell whether a function expects a vector, a data frame, grouped data, factors, or missing values. Good R docs should reveal the shape of the data early instead of treating it like background detail.

When a page jumps straight into mutate(), summarise(), or a modeling helper without showing the incoming data, readers are forced to reverse-engineer the example. That creates unnecessary friction. If the first few lines make the columns and types visible, the rest of the page becomes easier to follow.

library(dplyr)

sales <- tibble(
  region = c("north", "north", "south"),
  revenue = c(120, 180, 90)
)

sales %>%
  group_by(region) %>%
  summarise(total_revenue = sum(revenue), .groups = "drop")

This example works because the data is visible. A reader can see the grouping key, see the numeric column, and understand why the output changes shape after summarise(). The function is no longer floating in abstraction.

That matters because the R ecosystem spans several styles. Base R, tidyverse workflows, and modeling packages all carry different assumptions about inputs and outputs. Documentation should reduce that ambiguity quickly. If a function expects a factor or returns a tibble instead of a vector, show that near the start.

Showing the data shape early also improves transfer. Readers usually want to map the example onto their own dataset. They can only do that if they understand what the example starts with. A small visible table gives them the right mental substitution points.

This style helps maintainers too. Small, explicit examples are easier to review and easier to validate. They are also easier for automated systems to preserve accurately. If a repo is used to guide bulk content generation, data shape needs to be obvious enough that a weaker model does not invent it.

A practical rule is simple: if the example depends on a table, show the table shape before the transformation. If it depends on a vector, show the vector. If it depends on grouped data, make that grouping visible.

R documentation gets better when it starts where the reader’s uncertainty really starts: with the data itself. Once that is clear, the verbs and helpers make much more sense.

Why this matters for readers

Good documentation does not only transfer facts. It reduces hesitation. A reader should finish the first half of an article feeling more certain about what to try next, what kind of output to expect, and what mistakes are likely to happen. That is why strong examples matter so much. They shorten the path from recognition to execution.

In R, readers often arrive with partial context. They may know the language a bit but not the library, or they may know the problem but not the idiom. A solid article should therefore combine three things: a concrete example, a short explanation of what the example proves, and a note about where the pattern does or does not fit. That combination teaches more reliably than long exposition alone.

A practical writing pattern

A useful structure for articles is simple. Start with the smallest example that demonstrates the point. Then explain the important behavior in plain language. After that, add one or two variations that show how the same idea changes under slightly different conditions. This pattern is friendly to readers, but it is also friendly to maintenance. If the example changes later, the article can be updated without rewriting everything.

This is also exactly the kind of structure that helps automated content systems. When a repository contains clear, stable exemplars, weaker models have better odds of producing something serviceable instead of vague filler. In other words, good articles do double duty: they help humans now and they train the future shape of automated output.

What a strong seed article should do

For a seed article like this one, the goal is not to become the final word on the subject. The goal is to set a standard. It should show the expected frontmatter, a clean code block with a language tag, a readable narrative, and a tone that values concrete explanation over fluff. Once those pieces exist in the repo, future writing has something sane to imitate.

What shape information actually means

Shape of data refers to its dimensions, types, and column structure, the information you get from str(), dim(), and glimpse(). A function’s documentation that includes the expected shape of its input and output removes a class of errors that neither static type checking nor unit tests typically catch: mismatched column names, unexpected factor levels, date columns stored as character, integer columns where doubles are expected.

The canonical example: merge(df1, df2, by = "id"). Without seeing the shape of df1 and df2, a reader cannot know whether "id" is an integer, character, or factor — or whether both tables have it. A doc example that shows str(df1) before the merge, and str(result) after, communicates more than two paragraphs of prose description.

Implications for package development

R package developers using roxygen2 can add @param documentation that specifies the expected data structure: column names, types, and whether NA is allowed. Tools like checkmate and rlang::arg_match() enforce these contracts at runtime. But showing an example with glimpse(input) and glimpse(output) in the examples section is more readable than any formal type annotation.

The tibble print method, which shows column types below the column names, was designed partly to make shape visible at a glance. A documentation example that shows tibble output inherently shows the output shape. This is one reason tidyverse documentation tends to be easier to follow than base R documentation — the output format carries type information that the reader would otherwise need to infer.

Making shape visible in documentation

The simplest change is to include str() or glimpse() output as a comment in the first code block of a documentation example. Before calling a function, show what the input looks like. After the function, show what the output looks like. This costs a few extra lines and a few seconds of thought, but removes the ambiguity that forces readers to run the code themselves just to understand what type of object is involved.

For package vignettes, a “Data setup” section that shows the structure of the example data — column names, types, first few rows — before the analysis begins sets context that the rest of the vignette depends on. This is the convention in the tidyverse vignettes and one reason they are easier to follow than many base R vignettes.